github/ColossalAI
1
0
Fork 0
mirror of https://github.com/hpcaitech/ColossalAI.git synced 2025-09-05 19:13:01 +00:00
Code Issues Packages Projects Releases Wiki Activity

[misc] update pre-commit and run all files (#4752)

Browse Source 
* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
Download Patch File Download Diff File Expand all files Collapse all files

135
tests/kit/model_zoo/torchaudio/torchaudio.py
View File

@@ -23,24 +23,31 @@ def conformer_data_gen_fn():
transformer_output_transform_fn = lambda outputs: dict(frames=outputs[0], lengths=outputs[1])
model_zoo.register(name='torchaudio_conformer',
model_fn=lambda: tm.Conformer(
input_dim=INPUT_DIM, num_heads=4, ffn_dim=128, num_layers=4, depthwise_conv_kernel_size=31),
data_gen_fn=conformer_data_gen_fn,
output_transform_fn=transformer_output_transform_fn)
model_zoo.register(
name="torchaudio_conformer",
model_fn=lambda: tm.Conformer(
input_dim=INPUT_DIM, num_heads=4, ffn_dim=128, num_layers=4, depthwise_conv_kernel_size=31
),
data_gen_fn=conformer_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
)
single_output_transform_fn = lambda output: dict(output=output)
model_zoo.register(name='torchaudio_convtasnet',
model_fn=tm.ConvTasNet,
data_gen_fn=lambda: dict(input=torch.rand(4, 1, 8)),
output_transform_fn=single_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="torchaudio_convtasnet",
model_fn=tm.ConvTasNet,
data_gen_fn=lambda: dict(input=torch.rand(4, 1, 8)),
output_transform_fn=single_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(name='torchaudio_deepspeech',
model_fn=lambda: tm.DeepSpeech(IN_FEATURES, n_hidden=128, n_class=4),
data_gen_fn=lambda: dict(x=torch.rand(4, 1, 10, IN_FEATURES)),
output_transform_fn=single_output_transform_fn)
model_zoo.register(
name="torchaudio_deepspeech",
model_fn=lambda: tm.DeepSpeech(IN_FEATURES, n_hidden=128, n_class=4),
data_gen_fn=lambda: dict(x=torch.rand(4, 1, 10, IN_FEATURES)),
output_transform_fn=single_output_transform_fn,
)
def emformer_data_gen_fn():
@@ -50,21 +57,26 @@ def emformer_data_gen_fn():
model_zoo.register(
name='torchaudio_emformer',
name="torchaudio_emformer",
model_fn=lambda: tm.Emformer(input_dim=IN_FEATURES, num_heads=4, ffn_dim=128, num_layers=4, segment_length=4),
data_gen_fn=emformer_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(name='torchaudio_wav2letter_waveform',
model_fn=lambda: tm.Wav2Letter(input_type='waveform', num_features=40),
data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
output_transform_fn=single_output_transform_fn)
model_zoo.register(
name="torchaudio_wav2letter_waveform",
model_fn=lambda: tm.Wav2Letter(input_type="waveform", num_features=40),
data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
output_transform_fn=single_output_transform_fn,
)
model_zoo.register(name='torchaudio_wav2letter_mfcc',
model_fn=lambda: tm.Wav2Letter(input_type='mfcc', num_features=40),
data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
output_transform_fn=single_output_transform_fn)
model_zoo.register(
name="torchaudio_wav2letter_mfcc",
model_fn=lambda: tm.Wav2Letter(input_type="mfcc", num_features=40),
data_gen_fn=lambda: dict(x=torch.rand(4, 40, 400)),
output_transform_fn=single_output_transform_fn,
)
def wavernn_data_gen_fn():
@@ -73,20 +85,24 @@ def wavernn_data_gen_fn():
return dict(waveform=waveform, specgram=specgram)
model_zoo.register(name='torchaudio_wavernn',
model_fn=lambda: tm.WaveRNN(upsample_scales=[2, 2, 5],
n_classes=N_CLASSES,
hop_length=HOP_LENGTH,
kernel_size=KERNEL_SIZE,
n_freq=N_FREQ,
n_res_block=2,
n_rnn=64,
n_fc=64,
n_hidden=16,
n_output=16),
data_gen_fn=wavernn_data_gen_fn,
output_transform_fn=single_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="torchaudio_wavernn",
model_fn=lambda: tm.WaveRNN(
upsample_scales=[2, 2, 5],
n_classes=N_CLASSES,
hop_length=HOP_LENGTH,
kernel_size=KERNEL_SIZE,
n_freq=N_FREQ,
n_res_block=2,
n_rnn=64,
n_fc=64,
n_hidden=16,
n_output=16,
),
data_gen_fn=wavernn_data_gen_fn,
output_transform_fn=single_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
def tacotron_data_gen_fn():
@@ -97,17 +113,18 @@ def tacotron_data_gen_fn():
token_lengths = max_text_length * torch.ones((n_batch,))
mel_specgram = torch.rand(n_batch, N_MELS, max_mel_specgram_length)
mel_specgram_lengths = max_mel_specgram_length * torch.ones((n_batch,))
return dict(tokens=tokens,
token_lengths=token_lengths,
mel_specgram=mel_specgram,
mel_specgram_lengths=mel_specgram_lengths)
return dict(
tokens=tokens, token_lengths=token_lengths, mel_specgram=mel_specgram, mel_specgram_lengths=mel_specgram_lengths
)
model_zoo.register(name='torchaudio_tacotron',
model_fn=lambda: tm.Tacotron2(n_mels=N_MELS),
data_gen_fn=tacotron_data_gen_fn,
output_transform_fn=lambda outputs: dict(summed_output=sum(x.sum() for x in outputs)),
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="torchaudio_tacotron",
model_fn=lambda: tm.Tacotron2(n_mels=N_MELS),
data_gen_fn=tacotron_data_gen_fn,
output_transform_fn=lambda outputs: dict(summed_output=sum(x.sum() for x in outputs)),
model_attribute=ModelAttribute(has_control_flow=True),
)
def wav2vec_data_gen_fn():
@@ -117,14 +134,18 @@ def wav2vec_data_gen_fn():
return dict(waveforms=waveforms, lengths=lengths)
model_zoo.register(name='torchaudio_wav2vec2_base',
model_fn=partial(tm.wav2vec2_base, encoder_layer_drop=0.0),
data_gen_fn=wav2vec_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="torchaudio_wav2vec2_base",
model_fn=partial(tm.wav2vec2_base, encoder_layer_drop=0.0),
data_gen_fn=wav2vec_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)
model_zoo.register(name='torchaudio_hubert_base',
model_fn=tm.hubert_base,
data_gen_fn=wav2vec_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True))
model_zoo.register(
name="torchaudio_hubert_base",
model_fn=tm.hubert_base,
data_gen_fn=wav2vec_data_gen_fn,
output_transform_fn=transformer_output_transform_fn,
model_attribute=ModelAttribute(has_control_flow=True),
)